The adaptation layer aligns the teachers' and students' output feature dimensions via a 1 x 1 convolution kernel [1]} that generates a predefined output length with different input sizes. Let \(F_S\)  and \(F_{T_i}\)  be respectively the original features of the student and teacher \(T_i\) , and \(f_S\)  and \(f_{T_i}\)  their respective aligned features. In our implementation, \(f_S\)  and \(f_{T_i}\)  have the same size of \(F_S\)  and \(F_{T_i}\) .
